arXiv:2601.19920v1 Announce Type: new Abstract: Binary Neural Networks (BNNs), where weights and activations are constrained to binary values (+1, -1), are a highly efficient alternative to traditional neural networks. Unfortunately, typical BNNs, while binarizing linear layers (matrix-vector multiplication), still implement other network layers (batch normalization, softmax, output layer, and sometimes the input layer of a convolutional neural network) in full precision. This limits the area and energy benefits and requires architectural support for full precision operations. We propose […]

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arXiv:2603.06622v1 Announce Type: new Abstract: Accurate short-term power load forecasting is important to effectively manage, optimize, and ensure the robustness of modern power systems. This paper performs an empirical evaluation of a traditional statistical model and deep learning approaches for predicting short-term energy load. Four models, namely ARIMA, LSTM, BiLSTM, and Transformer, were leveraged on the PJM Hourly Energy Consumption data. The data processing involved interpolation, normalization, and a sliding-window sequence method. Each model’s forecasting performance was evaluated […]

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London’s 13th Tech Week has drawn to a close, bringing together technology and political leaders alongside scientists and investors from 130 countries.  This year’s expo unfolded against a backdrop of economic uncertainty, alongside increasing competition for AI investment as governments and businesses come to terms with the technology’s true potential. Declarations of investment in the UK’s AI sector showed that it is on the right path, but the country must do more to ensure it is not left […]

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arXiv:2601.21092v1 Announce Type: new Abstract: Planning effective interventions in biological systems requires treatment-effect models that adapt to unseen biological contexts by identifying their specific underlying mechanisms. Yet single-cell perturbation datasets span only a handful of biological contexts, and existing methods cannot leverage new interventional evidence at inference time to adapt beyond their training data. To meta-learn a perturbation effect estimator, we present MapPFN, a prior-data fitted network (PFN) pretrained on synthetic data generated from a prior over causal […]

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I’m a researcher and for my research I’m training a 14B-parameter model. However my available compute resources are limited to a single NVIDIA H100 GPU with 95 GB of VRAM provided by my institution via SSH. How do you all manage situations like this when working with large models? Please share your thoughts and experiences. submitted by /u/StatusArrival3382 [link] [comments]

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arXiv:2604.07494v1 Announce Type: new Abstract: Context: AI coding agents route every task to a single frontier large language model (LLM), paying premium inference cost even when many tasks are routine. Objectives: We propose Triage, a framework that uses code health metrics — indicators of software maintainability — as a routing signal to assign each task to the cheapest model tier whose output passes the same verification gate as the expensive model. Methods: Triage defines three capability tiers (light, […]

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We consider the general problem of learning a predictor that satisfies multiple objectives of interest simultaneously, a broad framework that captures a range of specific learning goals including calibration, regret, and multiaccuracy. We work in an online setting where the data distribution can change arbitrarily over time. Existing approaches to this problem aim to minimize the set of objectives over the entire time horizon in a worst-case sense, and in practice they do not necessarily adapt to distribution […]

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arXiv:2601.02381v1 Announce Type: new Abstract: Inductive cold-start recommendation remains the “Achilles’ Heel” of industrial academic platforms, where thousands of new scholars join daily without historical interaction records. While recent Generative Graph Models (e.g., HiGPT, OFA) demonstrate promising semantic capabilities, their prohibitive inference latency (often exceeding 13 minutes per 1,000 requests) and massive computational costs render them practically undeployable for real-time, million-scale applications. To bridge this gap between generative quality and industrial scalability, we propose TAG-HGT, a cost-effective neuro-symbolic […]

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Why do some interfaces feel like a natural extension of our thoughts, while others seem built just to cause us pain (remember those forms asking you to enter your birth date with a mile-long select menu instead of just letting you type it)? How do you create the kind of effortless interaction that makes a design truly great? Why have consumer apps already figured out this balance, while B2B software is still so painful? If you’ve traveled to […]

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